Overview

Dataset statistics

Number of variables19
Number of observations50000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory160.0 B

Variable types

Numeric11
Categorical8

Warnings

artist_name has a high cardinality: 6863 distinct values High cardinality
track_hash has a high cardinality: 43155 distinct values High cardinality
track_name has a high cardinality: 41699 distinct values High cardinality
tempo has a high cardinality: 29394 distinct values High cardinality
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
instrumentalness is highly correlated with loudnessHigh correlation
loudness is highly correlated with acousticness and 2 other fieldsHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with acousticness and 1 other fieldsHigh correlation
acousticness is highly correlated with energyHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with energyHigh correlation
speechiness is highly correlated with music_genreHigh correlation
loudness is highly correlated with instrumentalness and 4 other fieldsHigh correlation
instrumentalness is highly correlated with loudness and 1 other fieldsHigh correlation
valence is highly correlated with danceability and 1 other fieldsHigh correlation
acousticness is highly correlated with loudness and 2 other fieldsHigh correlation
music_genre is highly correlated with speechiness and 6 other fieldsHigh correlation
popularity is highly correlated with music_genreHigh correlation
danceability is highly correlated with loudness and 3 other fieldsHigh correlation
energy is highly correlated with loudness and 4 other fieldsHigh correlation
instance_id is uniformly distributed Uniform
track_hash is uniformly distributed Uniform
track_name is uniformly distributed Uniform
music_genre is uniformly distributed Uniform
instance_id has unique values Unique
popularity has 694 (1.4%) zeros Zeros
instrumentalness has 15001 (30.0%) zeros Zeros

Reproduction

Analysis started2021-09-09 16:41:17.664226
Analysis finished2021-09-09 16:41:53.997545
Duration36.33 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

instance_id
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct50000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55888.39636
Minimum20002
Maximum91759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2021-09-10T00:41:54.124544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20002
5-th percentile23561.95
Q137973.5
median55913.5
Q373863.25
95-th percentile88163.05
Maximum91759
Range71757
Interquartile range (IQR)35889.75

Descriptive statistics

Standard deviation20725.25625
Coefficient of variation (CV)0.3708329028
Kurtosis-1.200690581
Mean55888.39636
Median Absolute Deviation (MAD)17945.5
Skewness-0.001860513236
Sum2794419818
Variance429536246.7
MonotonicityNot monotonic
2021-09-10T00:41:54.298577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
858291
 
< 0.1%
273841
 
< 0.1%
314501
 
< 0.1%
294031
 
< 0.1%
826531
 
< 0.1%
887981
 
< 0.1%
867511
 
< 0.1%
437441
 
< 0.1%
416971
 
< 0.1%
478421
 
< 0.1%
Other values (49990)49990
> 99.9%
ValueCountFrequency (%)
200021
< 0.1%
200051
< 0.1%
200071
< 0.1%
200081
< 0.1%
200091
< 0.1%
200101
< 0.1%
200111
< 0.1%
200121
< 0.1%
200161
< 0.1%
200171
< 0.1%
ValueCountFrequency (%)
917591
< 0.1%
917581
< 0.1%
917571
< 0.1%
917541
< 0.1%
917531
< 0.1%
917521
< 0.1%
917511
< 0.1%
917491
< 0.1%
917481
< 0.1%
917471
< 0.1%

artist_name
Categorical

HIGH CARDINALITY

Distinct6863
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
empty_field
 
2489
Nobuo Uematsu
 
429
Wolfgang Amadeus Mozart
 
402
Ludwig van Beethoven
 
317
Johann Sebastian Bach
 
314
Other values (6858)
46049 

Length

Max length52
Median length11
Mean length11.89154
Min length1

Characters and Unicode

Total characters594577
Distinct characters208
Distinct categories15 ?
Distinct scripts6 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2346 ?
Unique (%)4.7%

Sample

1st rowAvenged Sevenfold
2nd rowBADBADNOTGOOD
3rd rowSystem Of A Down
4th rowJeremy Camp
5th rowThree Days Grace

Common Values

ValueCountFrequency (%)
empty_field2489
 
5.0%
Nobuo Uematsu429
 
0.9%
Wolfgang Amadeus Mozart402
 
0.8%
Ludwig van Beethoven317
 
0.6%
Johann Sebastian Bach314
 
0.6%
Frédéric Chopin241
 
0.5%
Drake172
 
0.3%
Capcom Sound Team169
 
0.3%
Yuki Hayashi167
 
0.3%
Eminem147
 
0.3%
Other values (6853)45153
90.3%

Length

2021-09-10T00:41:54.737545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the3675
 
3.8%
empty_field2489
 
2.6%
737
 
0.8%
band481
 
0.5%
nobuo429
 
0.4%
uematsu429
 
0.4%
of427
 
0.4%
wolfgang409
 
0.4%
amadeus402
 
0.4%
mozart402
 
0.4%
Other values (8118)86872
89.8%

Most occurring characters

ValueCountFrequency (%)
e52577
 
8.8%
46752
 
7.9%
a43628
 
7.3%
i35410
 
6.0%
o33338
 
5.6%
n32203
 
5.4%
r29404
 
4.9%
l24602
 
4.1%
t23091
 
3.9%
s21627
 
3.6%
Other values (198)251945
42.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter428594
72.1%
Uppercase Letter110102
 
18.5%
Space Separator46752
 
7.9%
Other Punctuation3165
 
0.5%
Connector Punctuation2489
 
0.4%
Decimal Number1637
 
0.3%
Dash Punctuation818
 
0.1%
Currency Symbol438
 
0.1%
Other Letter349
 
0.1%
Open Punctuation60
 
< 0.1%
Other values (5)173
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
25
 
7.2%
16
 
4.6%
16
 
4.6%
16
 
4.6%
16
 
4.6%
16
 
4.6%
12
 
3.4%
12
 
3.4%
12
 
3.4%
10
 
2.9%
Other values (62)198
56.7%
Lowercase Letter
ValueCountFrequency (%)
e52577
12.3%
a43628
 
10.2%
i35410
 
8.3%
o33338
 
7.8%
n32203
 
7.5%
r29404
 
6.9%
l24602
 
5.7%
t23091
 
5.4%
s21627
 
5.0%
h17722
 
4.1%
Other values (50)114992
26.8%
Uppercase Letter
ValueCountFrequency (%)
T9100
 
8.3%
S8601
 
7.8%
B8412
 
7.6%
A6966
 
6.3%
M6808
 
6.2%
C6758
 
6.1%
D5384
 
4.9%
J5303
 
4.8%
R5187
 
4.7%
L4971
 
4.5%
Other values (25)42612
38.7%
Other Punctuation
ValueCountFrequency (%)
.1503
47.5%
&659
20.8%
'370
 
11.7%
,316
 
10.0%
!162
 
5.1%
"72
 
2.3%
*32
 
1.0%
/20
 
0.6%
:15
 
0.5%
?10
 
0.3%
Other values (2)6
 
0.2%
Decimal Number
ValueCountFrequency (%)
1285
17.4%
2272
16.6%
0176
10.8%
9169
10.3%
7157
9.6%
5125
7.6%
3122
7.5%
6119
7.3%
4110
 
6.7%
8102
 
6.2%
Other Symbol
ValueCountFrequency (%)
12
30.8%
10
25.6%
10
25.6%
4
 
10.3%
3
 
7.7%
Math Symbol
ValueCountFrequency (%)
+55
94.8%
=2
 
3.4%
×1
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
-814
99.5%
4
 
0.5%
Open Punctuation
ValueCountFrequency (%)
[39
65.0%
(21
35.0%
Close Punctuation
ValueCountFrequency (%)
]39
65.0%
)21
35.0%
Space Separator
ValueCountFrequency (%)
46752
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2489
100.0%
Currency Symbol
ValueCountFrequency (%)
$438
100.0%
Modifier Letter
ValueCountFrequency (%)
7
100.0%
Final Punctuation
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin538671
90.6%
Common55527
 
9.3%
Han220
 
< 0.1%
Hiragana123
 
< 0.1%
Greek23
 
< 0.1%
Katakana13
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e52577
 
9.8%
a43628
 
8.1%
i35410
 
6.6%
o33338
 
6.2%
n32203
 
6.0%
r29404
 
5.5%
l24602
 
4.6%
t23091
 
4.3%
s21627
 
4.0%
h17722
 
3.3%
Other values (83)225069
41.8%
Han
ValueCountFrequency (%)
12
 
5.5%
12
 
5.5%
12
 
5.5%
10
 
4.5%
10
 
4.5%
10
 
4.5%
9
 
4.1%
8
 
3.6%
8
 
3.6%
7
 
3.2%
Other values (43)122
55.5%
Common
ValueCountFrequency (%)
46752
84.2%
_2489
 
4.5%
.1503
 
2.7%
-814
 
1.5%
&659
 
1.2%
$438
 
0.8%
'370
 
0.7%
,316
 
0.6%
1285
 
0.5%
2272
 
0.5%
Other values (31)1629
 
2.9%
Hiragana
ValueCountFrequency (%)
25
20.3%
16
13.0%
16
13.0%
16
13.0%
16
13.0%
16
13.0%
8
 
6.5%
4
 
3.3%
4
 
3.3%
1
 
0.8%
Katakana
ValueCountFrequency (%)
3
23.1%
2
15.4%
2
15.4%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
Greek
ValueCountFrequency (%)
μ23
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII592398
99.6%
Latin 1 Sup1621
 
0.3%
CJK213
 
< 0.1%
Latin Ext A123
 
< 0.1%
Hiragana123
 
< 0.1%
None30
 
< 0.1%
Misc Symbols20
 
< 0.1%
Punctuation17
 
< 0.1%
Katakana13
 
< 0.1%
Geometric Shapes12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e52577
 
8.9%
46752
 
7.9%
a43628
 
7.4%
i35410
 
6.0%
o33338
 
5.6%
n32203
 
5.4%
r29404
 
5.0%
l24602
 
4.2%
t23091
 
3.9%
s21627
 
3.7%
Other values (73)249766
42.2%
Latin 1 Sup
ValueCountFrequency (%)
é767
47.3%
ó171
 
10.5%
á153
 
9.4%
í114
 
7.0%
ö106
 
6.5%
ø34
 
2.1%
ü33
 
2.0%
ë31
 
1.9%
ÿ31
 
1.9%
Ó28
 
1.7%
Other values (21)153
 
9.4%
Dingbats
ValueCountFrequency (%)
4
57.1%
3
42.9%
Latin Ext A
ValueCountFrequency (%)
ř85
69.1%
š12
 
9.8%
č7
 
5.7%
ı5
 
4.1%
œ2
 
1.6%
ē2
 
1.6%
ł2
 
1.6%
ů2
 
1.6%
Ē2
 
1.6%
ć2
 
1.6%
Other values (2)2
 
1.6%
CJK
ValueCountFrequency (%)
12
 
5.6%
12
 
5.6%
12
 
5.6%
10
 
4.7%
10
 
4.7%
10
 
4.7%
9
 
4.2%
8
 
3.8%
8
 
3.8%
7
 
3.3%
Other values (42)115
54.0%
Geometric Shapes
ValueCountFrequency (%)
12
100.0%
Hiragana
ValueCountFrequency (%)
25
20.3%
16
13.0%
16
13.0%
16
13.0%
16
13.0%
16
13.0%
8
 
6.5%
4
 
3.3%
4
 
3.3%
1
 
0.8%
None
ValueCountFrequency (%)
μ23
76.7%
7
 
23.3%
Misc Symbols
ValueCountFrequency (%)
10
50.0%
10
50.0%
Katakana
ValueCountFrequency (%)
3
23.1%
2
15.4%
2
15.4%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
Punctuation
ValueCountFrequency (%)
9
52.9%
4
23.5%
4
23.5%

track_hash
Categorical

HIGH CARDINALITY
UNIFORM

Distinct43155
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
0wY9rA9fJkuESyYm9uzVK5
 
6
4YZbVct8l9MnAVIROnLQdx
 
6
1HwbgJAU9PZ7YbzKgVgoIF
 
6
0hK8IwYBQwGbTgd7C5XyRZ
 
6
4QbraaiC4oHW1qmWy7M3AI
 
5
Other values (43150)
49971 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1100000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37622 ?
Unique (%)75.2%

Sample

1st row0caajoOsHzQOZtIXitnRUN
2nd row0bdabO15YOj0iZPg2OujAw
3rd row0blIe8ZSUusQfh4hvBNWoD
4th row0CBM2iiBZmfKntDeQYboqU
5th row0c1gHntWjKD7QShC8s99sq

Common Values

ValueCountFrequency (%)
0wY9rA9fJkuESyYm9uzVK56
 
< 0.1%
4YZbVct8l9MnAVIROnLQdx6
 
< 0.1%
1HwbgJAU9PZ7YbzKgVgoIF6
 
< 0.1%
0hK8IwYBQwGbTgd7C5XyRZ6
 
< 0.1%
4QbraaiC4oHW1qmWy7M3AI5
 
< 0.1%
3td69vL9Py7Ai9wfXYnvji5
 
< 0.1%
1Ug1mV9h5qVSs4rvvnQWux5
 
< 0.1%
4QhWbupniDd44EDtnh2bFJ5
 
< 0.1%
4prEPl61C8qZpeo3IkYSMl5
 
< 0.1%
5IFCyWplye09HytIP80RCF5
 
< 0.1%
Other values (43145)49946
99.9%

Length

2021-09-10T00:41:55.250546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0wy9ra9fjkuesyym9uzvk56
 
< 0.1%
1hwbgjau9pz7ybzkgvgoif6
 
< 0.1%
0hk8iwybqwgbtgd7c5xyrz6
 
< 0.1%
4yzbvct8l9mnavironlqdx6
 
< 0.1%
3wicqmk8qurejpsjubipik5
 
< 0.1%
0hrd6csafhhqkptyfppmqh5
 
< 0.1%
4qbraaic4ohw1qmwy7m3ai5
 
< 0.1%
2wrzpld8qdrrxmxc63e5wj5
 
< 0.1%
3td69vl9py7ai9wfxynvji5
 
< 0.1%
4prepl61c8qzpeo3ikysml5
 
< 0.1%
Other values (43145)49946
99.9%

Most occurring characters

ValueCountFrequency (%)
129028
 
2.6%
027491
 
2.5%
325058
 
2.3%
222620
 
2.1%
422241
 
2.0%
722157
 
2.0%
521836
 
2.0%
W17496
 
1.6%
817475
 
1.6%
h17402
 
1.6%
Other values (52)877196
79.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter439790
40.0%
Lowercase Letter438436
39.9%
Decimal Number221774
20.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h17402
 
4.0%
x17380
 
4.0%
y17323
 
4.0%
i17305
 
3.9%
w17218
 
3.9%
v17105
 
3.9%
q17093
 
3.9%
j17054
 
3.9%
u17021
 
3.9%
t16990
 
3.9%
Other values (16)266545
60.8%
Uppercase Letter
ValueCountFrequency (%)
W17496
 
4.0%
Z17354
 
3.9%
I17317
 
3.9%
H17311
 
3.9%
X17250
 
3.9%
Y17208
 
3.9%
V17156
 
3.9%
B17097
 
3.9%
T17029
 
3.9%
U16986
 
3.9%
Other values (16)267586
60.8%
Decimal Number
ValueCountFrequency (%)
129028
13.1%
027491
12.4%
325058
11.3%
222620
10.2%
422241
10.0%
722157
10.0%
521836
9.8%
817475
7.9%
617050
7.7%
916818
7.6%

Most occurring scripts

ValueCountFrequency (%)
Latin878226
79.8%
Common221774
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
W17496
 
2.0%
h17402
 
2.0%
x17380
 
2.0%
Z17354
 
2.0%
y17323
 
2.0%
I17317
 
2.0%
H17311
 
2.0%
i17305
 
2.0%
X17250
 
2.0%
w17218
 
2.0%
Other values (42)704870
80.3%
Common
ValueCountFrequency (%)
129028
13.1%
027491
12.4%
325058
11.3%
222620
10.2%
422241
10.0%
722157
10.0%
521836
9.8%
817475
7.9%
617050
7.7%
916818
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
129028
 
2.6%
027491
 
2.5%
325058
 
2.3%
222620
 
2.1%
422241
 
2.0%
722157
 
2.0%
521836
 
2.0%
W17496
 
1.6%
817475
 
1.6%
h17402
 
1.6%
Other values (52)877196
79.7%

track_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct41699
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Home
 
16
Forever
 
15
Without You
 
14
Fire
 
13
Wake Up
 
13
Other values (41694)
49929 

Length

Max length250
Median length15
Mean length20.27006
Min length1

Characters and Unicode

Total characters1013503
Distinct characters1202
Distinct categories20 ?
Distinct scripts8 ?
Distinct blocks15 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35954 ?
Unique (%)71.9%

Sample

1st rowRequiem
2nd rowIn Your Eyes (feat. Charlotte Day Wilson)
3rd rowF**k The System
4th rowThere Will Be A Day
5th rowIt's All Over

Common Values

ValueCountFrequency (%)
Home16
 
< 0.1%
Forever15
 
< 0.1%
Without You14
 
< 0.1%
Fire13
 
< 0.1%
Wake Up13
 
< 0.1%
Summertime13
 
< 0.1%
Save Me12
 
< 0.1%
Echo12
 
< 0.1%
Dreams12
 
< 0.1%
Diamonds11
 
< 0.1%
Other values (41689)49869
99.7%

Length

2021-09-10T00:41:55.725550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7106
 
3.7%
the5588
 
2.9%
in3351
 
1.8%
feat2809
 
1.5%
i2376
 
1.3%
you2255
 
1.2%
no2216
 
1.2%
of1937
 
1.0%
a1918
 
1.0%
me1719
 
0.9%
Other values (25296)158625
83.5%

Most occurring characters

ValueCountFrequency (%)
139900
 
13.8%
e86010
 
8.5%
o60714
 
6.0%
a55964
 
5.5%
n50418
 
5.0%
i49929
 
4.9%
t44696
 
4.4%
r43037
 
4.2%
l30509
 
3.0%
s30453
 
3.0%
Other values (1192)421873
41.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter625136
61.7%
Uppercase Letter171201
 
16.9%
Space Separator139900
 
13.8%
Other Punctuation30561
 
3.0%
Decimal Number19271
 
1.9%
Other Letter7938
 
0.8%
Dash Punctuation6740
 
0.7%
Close Punctuation5919
 
0.6%
Open Punctuation5907
 
0.6%
Modifier Letter315
 
< 0.1%
Other values (10)615
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
370
 
4.7%
205
 
2.6%
139
 
1.8%
137
 
1.7%
133
 
1.7%
133
 
1.7%
127
 
1.6%
124
 
1.6%
112
 
1.4%
94
 
1.2%
Other values (969)6364
80.2%
Lowercase Letter
ValueCountFrequency (%)
e86010
13.8%
o60714
9.7%
a55964
 
9.0%
n50418
 
8.1%
i49929
 
8.0%
t44696
 
7.1%
r43037
 
6.9%
l30509
 
4.9%
s30453
 
4.9%
h22557
 
3.6%
Other values (81)150849
24.1%
Uppercase Letter
ValueCountFrequency (%)
S13878
 
8.1%
T13210
 
7.7%
M11838
 
6.9%
I11646
 
6.8%
A10581
 
6.2%
L9746
 
5.7%
B9173
 
5.4%
R8097
 
4.7%
C8012
 
4.7%
D7841
 
4.6%
Other values (40)67179
39.2%
Other Punctuation
ValueCountFrequency (%)
.11448
37.5%
,5652
18.5%
'4785
15.7%
:3818
 
12.5%
"1728
 
5.7%
&1092
 
3.6%
/791
 
2.6%
!570
 
1.9%
?276
 
0.9%
*132
 
0.4%
Other values (10)269
 
0.9%
Decimal Number
ValueCountFrequency (%)
14017
20.8%
23519
18.3%
02700
14.0%
31625
8.4%
41484
 
7.7%
51323
 
6.9%
91318
 
6.8%
61251
 
6.5%
71060
 
5.5%
8974
 
5.1%
Math Symbol
ValueCountFrequency (%)
~39
31.2%
+31
24.8%
|24
19.2%
<8
 
6.4%
>7
 
5.6%
=7
 
5.6%
4
 
3.2%
×3
 
2.4%
1
 
0.8%
1
 
0.8%
Other Symbol
ValueCountFrequency (%)
°7
38.9%
3
16.7%
®3
16.7%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Open Punctuation
ValueCountFrequency (%)
(5634
95.4%
[222
 
3.8%
40
 
0.7%
6
 
0.1%
3
 
0.1%
1
 
< 0.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
)5639
95.3%
]222
 
3.8%
45
 
0.8%
6
 
0.1%
4
 
0.1%
3
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-6662
98.8%
51
 
0.8%
17
 
0.3%
10
 
0.1%
Final Punctuation
ValueCountFrequency (%)
201
89.7%
21
 
9.4%
»2
 
0.9%
Initial Punctuation
ValueCountFrequency (%)
20
71.4%
6
 
21.4%
«2
 
7.1%
Modifier Letter
ValueCountFrequency (%)
305
96.8%
10
 
3.2%
Format
ValueCountFrequency (%)
10
62.5%
6
37.5%
Modifier Symbol
ValueCountFrequency (%)
´7
77.8%
^2
 
22.2%
Space Separator
ValueCountFrequency (%)
139900
100.0%
Currency Symbol
ValueCountFrequency (%)
$162
100.0%
Connector Punctuation
ValueCountFrequency (%)
_31
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Control
ValueCountFrequency (%)
’1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin796127
78.6%
Common209217
 
20.6%
Katakana3162
 
0.3%
Han2664
 
0.3%
Hiragana2113
 
0.2%
Cyrillic211
 
< 0.1%
Hebrew8
 
< 0.1%
Greek1
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
35
 
1.3%
34
 
1.3%
29
 
1.1%
29
 
1.1%
27
 
1.0%
24
 
0.9%
23
 
0.9%
22
 
0.8%
22
 
0.8%
22
 
0.8%
Other values (812)2397
90.0%
Latin
ValueCountFrequency (%)
e86010
 
10.8%
o60714
 
7.6%
a55964
 
7.0%
n50418
 
6.3%
i49929
 
6.3%
t44696
 
5.6%
r43037
 
5.4%
l30509
 
3.8%
s30453
 
3.8%
h22557
 
2.8%
Other values (94)321840
40.4%
Common
ValueCountFrequency (%)
139900
66.9%
.11448
 
5.5%
-6662
 
3.2%
,5652
 
2.7%
)5639
 
2.7%
(5634
 
2.7%
'4785
 
2.3%
14017
 
1.9%
:3818
 
1.8%
23519
 
1.7%
Other values (70)18143
 
8.7%
Katakana
ValueCountFrequency (%)
205
 
6.5%
139
 
4.4%
137
 
4.3%
133
 
4.2%
133
 
4.2%
127
 
4.0%
94
 
3.0%
91
 
2.9%
81
 
2.6%
81
 
2.6%
Other values (68)1941
61.4%
Hiragana
ValueCountFrequency (%)
370
 
17.5%
124
 
5.9%
112
 
5.3%
92
 
4.4%
87
 
4.1%
76
 
3.6%
69
 
3.3%
55
 
2.6%
53
 
2.5%
53
 
2.5%
Other values (62)1022
48.4%
Cyrillic
ValueCountFrequency (%)
о25
 
11.8%
а18
 
8.5%
и16
 
7.6%
н15
 
7.1%
с14
 
6.6%
е13
 
6.2%
к11
 
5.2%
т10
 
4.7%
м7
 
3.3%
р7
 
3.3%
Other values (28)75
35.5%
Hebrew
ValueCountFrequency (%)
ל2
25.0%
ר1
12.5%
ק1
12.5%
ב1
12.5%
י1
12.5%
ו1
12.5%
ת1
12.5%
Greek
ValueCountFrequency (%)
μ1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1003131
99.0%
Katakana3557
 
0.4%
CJK2654
 
0.3%
Hiragana2113
 
0.2%
Latin 1 Sup1299
 
0.1%
Punctuation299
 
< 0.1%
Cyrillic211
 
< 0.1%
None199
 
< 0.1%
Latin Ext A17
 
< 0.1%
Hebrew8
 
< 0.1%
Other values (5)15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
139900
 
13.9%
e86010
 
8.6%
o60714
 
6.1%
a55964
 
5.6%
n50418
 
5.0%
i49929
 
5.0%
t44696
 
4.5%
r43037
 
4.3%
l30509
 
3.0%
s30453
 
3.0%
Other values (81)411501
41.0%
Latin 1 Sup
ValueCountFrequency (%)
é374
28.8%
è108
 
8.3%
ü107
 
8.2%
ö92
 
7.1%
ä70
 
5.4%
í66
 
5.1%
ó65
 
5.0%
á54
 
4.2%
à51
 
3.9%
ñ45
 
3.5%
Other values (42)267
20.6%
Punctuation
ValueCountFrequency (%)
201
67.2%
21
 
7.0%
20
 
6.7%
17
 
5.7%
10
 
3.3%
10
 
3.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
1
 
0.3%
Katakana
ValueCountFrequency (%)
305
 
8.6%
205
 
5.8%
139
 
3.9%
137
 
3.9%
133
 
3.7%
133
 
3.7%
127
 
3.6%
94
 
2.6%
91
 
2.6%
90
 
2.5%
Other values (70)2103
59.1%
CJK
ValueCountFrequency (%)
35
 
1.3%
34
 
1.3%
29
 
1.1%
29
 
1.1%
27
 
1.0%
24
 
0.9%
23
 
0.9%
22
 
0.8%
22
 
0.8%
22
 
0.8%
Other values (811)2387
89.9%
Hiragana
ValueCountFrequency (%)
370
 
17.5%
124
 
5.9%
112
 
5.3%
92
 
4.4%
87
 
4.1%
76
 
3.6%
69
 
3.3%
55
 
2.6%
53
 
2.5%
53
 
2.5%
Other values (62)1022
48.4%
None
ValueCountFrequency (%)
51
25.6%
45
22.6%
40
20.1%
21
10.6%
10
 
5.0%
8
 
4.0%
6
 
3.0%
6
 
3.0%
4
 
2.0%
3
 
1.5%
Other values (3)5
 
2.5%
Misc Symbols
ValueCountFrequency (%)
3
42.9%
1
 
14.3%
1
 
14.3%
1
 
14.3%
1
 
14.3%
Arrows
ValueCountFrequency (%)
1
50.0%
1
50.0%
Math Operators
ValueCountFrequency (%)
4
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Geometric Shapes
ValueCountFrequency (%)
1
100.0%
Latin Ext A
ValueCountFrequency (%)
š7
41.2%
ř3
17.6%
ě2
 
11.8%
ō1
 
5.9%
Ž1
 
5.9%
ž1
 
5.9%
Č1
 
5.9%
č1
 
5.9%
Cyrillic
ValueCountFrequency (%)
о25
 
11.8%
а18
 
8.5%
и16
 
7.6%
н15
 
7.1%
с14
 
6.6%
е13
 
6.2%
к11
 
5.2%
т10
 
4.7%
м7
 
3.3%
р7
 
3.3%
Other values (28)75
35.5%
Hebrew
ValueCountFrequency (%)
ל2
25.0%
ר1
12.5%
ק1
12.5%
ב1
12.5%
י1
12.5%
ו1
12.5%
ת1
12.5%

popularity
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct99
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.22042
Minimum0
Maximum99
Zeros694
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2021-09-10T00:41:56.047577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q134
median45
Q356
95-th percentile68
Maximum99
Range99
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.54200843
Coefficient of variation (CV)0.3514667756
Kurtosis0.01346140335
Mean44.22042
Median Absolute Deviation (MAD)11
Skewness-0.3047913763
Sum2211021
Variance241.5540261
MonotonicityNot monotonic
2021-09-10T00:41:56.237545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521316
 
2.6%
541295
 
2.6%
531286
 
2.6%
501265
 
2.5%
551250
 
2.5%
511229
 
2.5%
561222
 
2.4%
381212
 
2.4%
361197
 
2.4%
371193
 
2.4%
Other values (89)37535
75.1%
ValueCountFrequency (%)
0694
1.4%
131
 
0.1%
251
 
0.1%
343
 
0.1%
442
 
0.1%
527
 
0.1%
614
 
< 0.1%
721
 
< 0.1%
845
 
0.1%
957
 
0.1%
ValueCountFrequency (%)
991
 
< 0.1%
971
 
< 0.1%
962
 
< 0.1%
952
 
< 0.1%
941
 
< 0.1%
932
 
< 0.1%
921
 
< 0.1%
912
 
< 0.1%
907
< 0.1%
898
< 0.1%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4193
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3063827163
Minimum0
Maximum0.996
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2021-09-10T00:41:56.423576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000393
Q10.02
median0.144
Q30.552
95-th percentile0.978
Maximum0.996
Range0.996
Interquartile range (IQR)0.532

Descriptive statistics

Standard deviation0.3413400065
Coefficient of variation (CV)1.114096809
Kurtosis-0.7216753954
Mean0.3063827163
Median Absolute Deviation (MAD)0.141165
Skewness0.8823528035
Sum15319.13582
Variance0.116513
MonotonicityNot monotonic
2021-09-10T00:41:56.612576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995278
 
0.6%
0.994240
 
0.5%
0.992215
 
0.4%
0.993198
 
0.4%
0.991158
 
0.3%
0.99158
 
0.3%
0.989136
 
0.3%
0.985119
 
0.2%
0.982117
 
0.2%
0.987116
 
0.2%
Other values (4183)48265
96.5%
ValueCountFrequency (%)
01
 
< 0.1%
1.02 × 10-61
 
< 0.1%
1.27 × 10-61
 
< 0.1%
1.37 × 10-61
 
< 0.1%
1.38 × 10-61
 
< 0.1%
1.39 × 10-63
< 0.1%
1.46 × 10-61
 
< 0.1%
1.55 × 10-61
 
< 0.1%
1.57 × 10-61
 
< 0.1%
1.6 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.99683
 
0.2%
0.995278
0.6%
0.994240
0.5%
0.993198
0.4%
0.992215
0.4%
0.991158
0.3%
0.99158
0.3%
0.989136
0.3%
0.988103
 
0.2%
0.987116
0.2%

danceability
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1088
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.558240944
Minimum0.0596
Maximum0.986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2021-09-10T00:41:56.803545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0596
5-th percentile0.233
Q10.442
median0.568
Q30.687
95-th percentile0.837
Maximum0.986
Range0.9264
Interquartile range (IQR)0.245

Descriptive statistics

Standard deviation0.1786319471
Coefficient of variation (CV)0.3199907657
Kurtosis-0.3006361617
Mean0.558240944
Median Absolute Deviation (MAD)0.122
Skewness-0.2999180105
Sum27912.0472
Variance0.03190937254
MonotonicityNot monotonic
2021-09-10T00:41:56.992577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.529143
 
0.3%
0.657139
 
0.3%
0.61134
 
0.3%
0.554133
 
0.3%
0.547130
 
0.3%
0.499130
 
0.3%
0.589129
 
0.3%
0.628128
 
0.3%
0.576127
 
0.3%
0.644126
 
0.3%
Other values (1078)48681
97.4%
ValueCountFrequency (%)
0.05961
< 0.1%
0.061
< 0.1%
0.06021
< 0.1%
0.06062
< 0.1%
0.06072
< 0.1%
0.0611
< 0.1%
0.06131
< 0.1%
0.06141
< 0.1%
0.06161
< 0.1%
0.06171
< 0.1%
ValueCountFrequency (%)
0.9861
< 0.1%
0.982
< 0.1%
0.9791
< 0.1%
0.9782
< 0.1%
0.9772
< 0.1%
0.9761
< 0.1%
0.9751
< 0.1%
0.9741
< 0.1%
0.9731
< 0.1%
0.9722
< 0.1%

duration_ms
Real number (ℝ)

Distinct26028
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221252.6029
Minimum-1
Maximum4830606
Zeros0
Zeros (%)0.0%
Negative4939
Negative (%)9.9%
Memory size781.2 KiB
2021-09-10T00:41:57.180577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1174800
median219281
Q3268612.25
95-th percentile401497.05
Maximum4830606
Range4830607
Interquartile range (IQR)93812.25

Descriptive statistics

Standard deviation128671.9572
Coefficient of variation (CV)0.5815613263
Kurtosis98.16617277
Mean221252.6029
Median Absolute Deviation (MAD)46866
Skewness4.264927735
Sum1.106263014 × 1010
Variance1.655647256 × 1010
MonotonicityNot monotonic
2021-09-10T00:41:57.368578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-14939
 
9.9%
24000033
 
0.1%
19200032
 
0.1%
18000028
 
0.1%
21600020
 
< 0.1%
20800019
 
< 0.1%
18600019
 
< 0.1%
18560017
 
< 0.1%
22800016
 
< 0.1%
23040016
 
< 0.1%
Other values (26018)44861
89.7%
ValueCountFrequency (%)
-14939
9.9%
155091
 
< 0.1%
163161
 
< 0.1%
196931
 
< 0.1%
200961
 
< 0.1%
227501
 
< 0.1%
240001
 
< 0.1%
262931
 
< 0.1%
267171
 
< 0.1%
271791
 
< 0.1%
ValueCountFrequency (%)
48306061
< 0.1%
44979941
< 0.1%
42760001
< 0.1%
31954401
< 0.1%
27649341
< 0.1%
22940001
< 0.1%
20192931
< 0.1%
19357871
< 0.1%
18726531
< 0.1%
17784401
< 0.1%

energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2085
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5997547876
Minimum0.000792
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2021-09-10T00:41:57.559542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.000792
5-th percentile0.0728
Q10.433
median0.643
Q30.815
95-th percentile0.953
Maximum0.999
Range0.998208
Interquartile range (IQR)0.382

Descriptive statistics

Standard deviation0.2645592667
Coefficient of variation (CV)0.4411123882
Kurtosis-0.5943393708
Mean0.5997547876
Median Absolute Deviation (MAD)0.187
Skewness-0.5702833972
Sum29987.73938
Variance0.06999160558
MonotonicityNot monotonic
2021-09-10T00:41:57.785554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.805103
 
0.2%
0.675103
 
0.2%
0.7299
 
0.2%
0.85998
 
0.2%
0.8396
 
0.2%
0.90293
 
0.2%
0.82492
 
0.2%
0.83391
 
0.2%
0.85690
 
0.2%
0.93990
 
0.2%
Other values (2075)49045
98.1%
ValueCountFrequency (%)
0.0007921
< 0.1%
0.0007951
< 0.1%
0.0008251
< 0.1%
0.00091
< 0.1%
0.0009431
< 0.1%
0.0009531
< 0.1%
0.001011
< 0.1%
0.001041
< 0.1%
0.001062
< 0.1%
0.001081
< 0.1%
ValueCountFrequency (%)
0.9995
 
< 0.1%
0.99816
 
< 0.1%
0.99718
 
< 0.1%
0.99630
0.1%
0.99546
0.1%
0.99436
0.1%
0.99330
0.1%
0.99237
0.1%
0.99151
0.1%
0.9944
0.1%

instrumentalness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct5131
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1816006815
Minimum0
Maximum0.996
Zeros15001
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2021-09-10T00:41:57.998576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000158
Q30.155
95-th percentile0.908
Maximum0.996
Range0.996
Interquartile range (IQR)0.155

Descriptive statistics

Standard deviation0.3254090738
Coefficient of variation (CV)1.791893462
Kurtosis0.4567189695
Mean0.1816006815
Median Absolute Deviation (MAD)0.000158
Skewness1.487047516
Sum9080.034076
Variance0.1058910653
MonotonicityNot monotonic
2021-09-10T00:41:58.191578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015001
30.0%
0.89870
 
0.1%
0.90269
 
0.1%
0.91266
 
0.1%
0.89766
 
0.1%
0.89165
 
0.1%
0.961
 
0.1%
0.91461
 
0.1%
0.91761
 
0.1%
0.92361
 
0.1%
Other values (5121)34419
68.8%
ValueCountFrequency (%)
015001
30.0%
1 × 10-63
 
< 0.1%
1.01 × 10-626
 
0.1%
1.02 × 10-618
 
< 0.1%
1.03 × 10-619
 
< 0.1%
1.04 × 10-619
 
< 0.1%
1.05 × 10-619
 
< 0.1%
1.06 × 10-616
 
< 0.1%
1.07 × 10-619
 
< 0.1%
1.08 × 10-616
 
< 0.1%
ValueCountFrequency (%)
0.9961
 
< 0.1%
0.9941
 
< 0.1%
0.9932
 
< 0.1%
0.9922
 
< 0.1%
0.9891
 
< 0.1%
0.9883
< 0.1%
0.9874
< 0.1%
0.9863
< 0.1%
0.9855
< 0.1%
0.9845
< 0.1%

key
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
G
5727 
C
5522 
C#
5405 
D
5265 
A
4825 
Other values (7)
23256 

Length

Max length2
Median length1
Mean length1.33542
Min length1

Characters and Unicode

Total characters66771
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG#
2nd rowC
3rd rowG#
4th rowF#
5th rowC

Common Values

ValueCountFrequency (%)
G5727
11.5%
C5522
11.0%
C#5405
10.8%
D5265
10.5%
A4825
9.7%
F4341
8.7%
B3789
7.6%
E3760
7.5%
A#3356
6.7%
G#3319
6.6%
Other values (2)4691
9.4%

Length

2021-09-10T00:41:58.546547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c10927
21.9%
g9046
18.1%
a8181
16.4%
f7442
14.9%
d6855
13.7%
b3789
 
7.6%
e3760
 
7.5%

Most occurring characters

ValueCountFrequency (%)
#16771
25.1%
C10927
16.4%
G9046
13.5%
A8181
12.3%
F7442
11.1%
D6855
10.3%
B3789
 
5.7%
E3760
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter50000
74.9%
Other Punctuation16771
 
25.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C10927
21.9%
G9046
18.1%
A8181
16.4%
F7442
14.9%
D6855
13.7%
B3789
 
7.6%
E3760
 
7.5%
Other Punctuation
ValueCountFrequency (%)
#16771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin50000
74.9%
Common16771
 
25.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
C10927
21.9%
G9046
18.1%
A8181
16.4%
F7442
14.9%
D6855
13.7%
B3789
 
7.6%
E3760
 
7.5%
Common
ValueCountFrequency (%)
#16771
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII66771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
#16771
25.1%
C10927
16.4%
G9046
13.5%
A8181
12.3%
F7442
11.1%
D6855
10.3%
B3789
 
5.7%
E3760
 
5.6%

liveness
Real number (ℝ≥0)

Distinct1646
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1938964414
Minimum0.00967
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2021-09-10T00:41:58.769542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.00967
5-th percentile0.0624
Q10.0969
median0.126
Q30.244
95-th percentile0.551
Maximum1
Range0.99033
Interquartile range (IQR)0.1471

Descriptive statistics

Standard deviation0.1616370683
Coefficient of variation (CV)0.8336257597
Kurtosis5.710038571
Mean0.1938964414
Median Absolute Deviation (MAD)0.0441
Skewness2.249483817
Sum9694.82207
Variance0.02612654184
MonotonicityNot monotonic
2021-09-10T00:41:59.225577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11625
 
1.2%
0.108610
 
1.2%
0.111609
 
1.2%
0.109552
 
1.1%
0.107542
 
1.1%
0.112527
 
1.1%
0.104510
 
1.0%
0.105489
 
1.0%
0.106487
 
1.0%
0.103481
 
1.0%
Other values (1636)44568
89.1%
ValueCountFrequency (%)
0.009671
< 0.1%
0.01361
< 0.1%
0.01571
< 0.1%
0.01691
< 0.1%
0.01731
< 0.1%
0.01882
< 0.1%
0.01911
< 0.1%
0.01941
< 0.1%
0.01961
< 0.1%
0.02041
< 0.1%
ValueCountFrequency (%)
12
< 0.1%
0.9961
 
< 0.1%
0.9931
 
< 0.1%
0.9921
 
< 0.1%
0.9912
< 0.1%
0.993
< 0.1%
0.9892
< 0.1%
0.9883
< 0.1%
0.9872
< 0.1%
0.9862
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17247
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.1337611
Minimum-47.046
Maximum3.744
Zeros1
Zeros (%)< 0.1%
Negative49952
Negative (%)99.9%
Memory size781.2 KiB
2021-09-10T00:41:59.411576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-47.046
5-th percentile-22.96625
Q1-10.86
median-7.2765
Q3-5.173
95-th percentile-3.065
Maximum3.744
Range50.79
Interquartile range (IQR)5.687

Descriptive statistics

Standard deviation6.162989636
Coefficient of variation (CV)-0.6747482848
Kurtosis4.004900246
Mean-9.1337611
Median Absolute Deviation (MAD)2.5445
Skewness-1.87135502
Sum-456688.055
Variance37.98244125
MonotonicityNot monotonic
2021-09-10T00:41:59.648552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.44319
 
< 0.1%
-5.13317
 
< 0.1%
-7.06617
 
< 0.1%
-5.01316
 
< 0.1%
-5.98216
 
< 0.1%
-5.60616
 
< 0.1%
-5.58716
 
< 0.1%
-6.21715
 
< 0.1%
-4.15915
 
< 0.1%
-5.01615
 
< 0.1%
Other values (17237)49838
99.7%
ValueCountFrequency (%)
-47.0461
< 0.1%
-46.9851
< 0.1%
-46.5071
< 0.1%
-46.1221
< 0.1%
-46.0521
< 0.1%
-44.4061
< 0.1%
-44.1081
< 0.1%
-44.0461
< 0.1%
-43.4071
< 0.1%
-43.1351
< 0.1%
ValueCountFrequency (%)
3.7441
< 0.1%
1.9491
< 0.1%
1.8931
< 0.1%
1.611
< 0.1%
1.5851
< 0.1%
1.3421
< 0.1%
1.3141
< 0.1%
1.2751
< 0.1%
1.0231
< 0.1%
1.0121
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Major
32099 
Minor
17901 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters250000
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor
2nd rowMinor
3rd rowMajor
4th rowMajor
5th rowMinor

Common Values

ValueCountFrequency (%)
Major32099
64.2%
Minor17901
35.8%

Length

2021-09-10T00:42:00.066546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-10T00:42:00.166579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
major32099
64.2%
minor17901
35.8%

Most occurring characters

ValueCountFrequency (%)
M50000
20.0%
o50000
20.0%
r50000
20.0%
a32099
12.8%
j32099
12.8%
i17901
 
7.2%
n17901
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter200000
80.0%
Uppercase Letter50000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o50000
25.0%
r50000
25.0%
a32099
16.0%
j32099
16.0%
i17901
 
9.0%
n17901
 
9.0%
Uppercase Letter
ValueCountFrequency (%)
M50000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin250000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M50000
20.0%
o50000
20.0%
r50000
20.0%
a32099
12.8%
j32099
12.8%
i17901
 
7.2%
n17901
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII250000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M50000
20.0%
o50000
20.0%
r50000
20.0%
a32099
12.8%
j32099
12.8%
i17901
 
7.2%
n17901
 
7.2%

speechiness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1337
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.093586474
Minimum0.0223
Maximum0.942
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2021-09-10T00:42:00.294577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0223
5-th percentile0.0281
Q10.0361
median0.0489
Q30.098525
95-th percentile0.323
Maximum0.942
Range0.9197
Interquartile range (IQR)0.062425

Descriptive statistics

Standard deviation0.1013731809
Coefficient of variation (CV)1.083203336
Kurtosis7.413530414
Mean0.093586474
Median Absolute Deviation (MAD)0.017
Skewness2.47477924
Sum4679.3237
Variance0.0102765218
MonotonicityNot monotonic
2021-09-10T00:42:00.479577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0332173
 
0.3%
0.0337155
 
0.3%
0.0315153
 
0.3%
0.0329152
 
0.3%
0.0362148
 
0.3%
0.0336147
 
0.3%
0.0358146
 
0.3%
0.0363145
 
0.3%
0.0335143
 
0.3%
0.0343143
 
0.3%
Other values (1327)48495
97.0%
ValueCountFrequency (%)
0.02231
 
< 0.1%
0.02243
 
< 0.1%
0.02251
 
< 0.1%
0.02262
 
< 0.1%
0.02271
 
< 0.1%
0.02286
< 0.1%
0.02293
 
< 0.1%
0.0232
 
< 0.1%
0.02318
< 0.1%
0.02325
< 0.1%
ValueCountFrequency (%)
0.9421
 
< 0.1%
0.9411
 
< 0.1%
0.9391
 
< 0.1%
0.9321
 
< 0.1%
0.9271
 
< 0.1%
0.9223
< 0.1%
0.921
 
< 0.1%
0.9182
< 0.1%
0.9041
 
< 0.1%
0.8891
 
< 0.1%

tempo
Categorical

HIGH CARDINALITY

Distinct29394
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
?
4980 
120.0
 
17
140.007
 
17
100.00299999999999
 
16
130.016
 
15
Other values (29389)
44955 

Length

Max length18
Median length7
Mean length8.40992
Min length1

Characters and Unicode

Total characters420496
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20719 ?
Unique (%)41.4%

Sample

1st row142.016
2nd row79.71
3rd row171.433
4th row77.291
5th row?

Common Values

ValueCountFrequency (%)
?4980
 
10.0%
120.017
 
< 0.1%
140.00717
 
< 0.1%
100.0029999999999916
 
< 0.1%
130.01615
 
< 0.1%
100.0020000000000115
 
< 0.1%
120.01515
 
< 0.1%
100.01415
 
< 0.1%
120.00514
 
< 0.1%
95.00614
 
< 0.1%
Other values (29384)44882
89.8%

Length

2021-09-10T00:42:00.874578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4980
 
10.0%
120.017
 
< 0.1%
140.00717
 
< 0.1%
100.0029999999999916
 
< 0.1%
100.01415
 
< 0.1%
100.0020000000000115
 
< 0.1%
130.01615
 
< 0.1%
120.01515
 
< 0.1%
120.00514
 
< 0.1%
119.98514
 
< 0.1%
Other values (29384)44882
89.8%

Most occurring characters

ValueCountFrequency (%)
991131
21.7%
080520
19.1%
155478
13.2%
.45020
10.7%
224202
 
5.8%
722384
 
5.3%
822345
 
5.3%
319087
 
4.5%
519025
 
4.5%
418995
 
4.5%
Other values (2)22309
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number370496
88.1%
Other Punctuation50000
 
11.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
991131
24.6%
080520
21.7%
155478
15.0%
224202
 
6.5%
722384
 
6.0%
822345
 
6.0%
319087
 
5.2%
519025
 
5.1%
418995
 
5.1%
617329
 
4.7%
Other Punctuation
ValueCountFrequency (%)
.45020
90.0%
?4980
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common420496
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
991131
21.7%
080520
19.1%
155478
13.2%
.45020
10.7%
224202
 
5.8%
722384
 
5.3%
822345
 
5.3%
319087
 
4.5%
519025
 
4.5%
418995
 
4.5%
Other values (2)22309
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII420496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
991131
21.7%
080520
19.1%
155478
13.2%
.45020
10.7%
224202
 
5.8%
722384
 
5.3%
822345
 
5.3%
319087
 
4.5%
519025
 
4.5%
418995
 
4.5%
Other values (2)22309
 
5.3%

obtained_date
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
4-Apr
44748 
3-Apr
 
4067
5-Apr
 
784
1-Apr
 
400
0/4
 
1

Length

Max length5
Median length5
Mean length4.99996
Min length3

Characters and Unicode

Total characters249998
Distinct characters10
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4-Apr
2nd row4-Apr
3rd row4-Apr
4th row4-Apr
5th row4-Apr

Common Values

ValueCountFrequency (%)
4-Apr44748
89.5%
3-Apr4067
 
8.1%
5-Apr784
 
1.6%
1-Apr400
 
0.8%
0/41
 
< 0.1%

Length

2021-09-10T00:42:01.211574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-10T00:42:01.332577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
4-apr44748
89.5%
3-apr4067
 
8.1%
5-apr784
 
1.6%
1-apr400
 
0.8%
0/41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
-49999
20.0%
A49999
20.0%
p49999
20.0%
r49999
20.0%
444749
17.9%
34067
 
1.6%
5784
 
0.3%
1400
 
0.2%
01
 
< 0.1%
/1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter99998
40.0%
Decimal Number50001
20.0%
Dash Punctuation49999
20.0%
Uppercase Letter49999
20.0%
Other Punctuation1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
444749
89.5%
34067
 
8.1%
5784
 
1.6%
1400
 
0.8%
01
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
p49999
50.0%
r49999
50.0%
Dash Punctuation
ValueCountFrequency (%)
-49999
100.0%
Uppercase Letter
ValueCountFrequency (%)
A49999
100.0%
Other Punctuation
ValueCountFrequency (%)
/1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin149997
60.0%
Common100001
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-49999
50.0%
444749
44.7%
34067
 
4.1%
5784
 
0.8%
1400
 
0.4%
01
 
< 0.1%
/1
 
< 0.1%
Latin
ValueCountFrequency (%)
A49999
33.3%
p49999
33.3%
r49999
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII249998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-49999
20.0%
A49999
20.0%
p49999
20.0%
r49999
20.0%
444749
17.9%
34067
 
1.6%
5784
 
0.3%
1400
 
0.2%
01
 
< 0.1%
/1
 
< 0.1%

valence
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1615
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.456264486
Minimum0
Maximum0.992
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2021-09-10T00:42:01.504542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0664
Q10.257
median0.448
Q30.648
95-th percentile0.877
Maximum0.992
Range0.992
Interquartile range (IQR)0.391

Descriptive statistics

Standard deviation0.2471187357
Coefficient of variation (CV)0.5416129095
Kurtosis-0.9321348606
Mean0.456264486
Median Absolute Deviation (MAD)0.195
Skewness0.1324011837
Sum22813.2243
Variance0.06106766955
MonotonicityNot monotonic
2021-09-10T00:42:01.698546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.338100
 
0.2%
0.32495
 
0.2%
0.33293
 
0.2%
0.35191
 
0.2%
0.3787
 
0.2%
0.35886
 
0.2%
0.36285
 
0.2%
0.34784
 
0.2%
0.30484
 
0.2%
0.39283
 
0.2%
Other values (1605)49112
98.2%
ValueCountFrequency (%)
02
< 0.1%
0.01931
< 0.1%
0.02051
< 0.1%
0.02341
< 0.1%
0.02411
< 0.1%
0.02461
< 0.1%
0.02471
< 0.1%
0.02511
< 0.1%
0.02521
< 0.1%
0.02591
< 0.1%
ValueCountFrequency (%)
0.9921
 
< 0.1%
0.991
 
< 0.1%
0.9891
 
< 0.1%
0.9871
 
< 0.1%
0.9861
 
< 0.1%
0.9854
< 0.1%
0.9841
 
< 0.1%
0.9831
 
< 0.1%
0.9823
< 0.1%
0.9811
 
< 0.1%

music_genre
Categorical

HIGH CORRELATION
UNIFORM

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Country
5000 
Blues
5000 
Anime
5000 
Electronic
5000 
Rap
5000 
Other values (5)
25000 

Length

Max length11
Median length6
Mean length6.5
Min length3

Characters and Unicode

Total characters325000
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlternative
2nd rowAlternative
3rd rowAlternative
4th rowAlternative
5th rowAlternative

Common Values

ValueCountFrequency (%)
Country5000
10.0%
Blues5000
10.0%
Anime5000
10.0%
Electronic5000
10.0%
Rap5000
10.0%
Hip-Hop5000
10.0%
Rock5000
10.0%
Classical5000
10.0%
Alternative5000
10.0%
Jazz5000
10.0%

Length

2021-09-10T00:42:02.063578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-10T00:42:02.202543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
alternative5000
10.0%
classical5000
10.0%
anime5000
10.0%
jazz5000
10.0%
country5000
10.0%
rap5000
10.0%
electronic5000
10.0%
hip-hop5000
10.0%
blues5000
10.0%
rock5000
10.0%

Most occurring characters

ValueCountFrequency (%)
l25000
 
7.7%
e25000
 
7.7%
a25000
 
7.7%
i25000
 
7.7%
t20000
 
6.2%
n20000
 
6.2%
c20000
 
6.2%
o20000
 
6.2%
r15000
 
4.6%
s15000
 
4.6%
Other values (15)115000
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter265000
81.5%
Uppercase Letter55000
 
16.9%
Dash Punctuation5000
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l25000
9.4%
e25000
9.4%
a25000
9.4%
i25000
9.4%
t20000
 
7.5%
n20000
 
7.5%
c20000
 
7.5%
o20000
 
7.5%
r15000
 
5.7%
s15000
 
5.7%
Other values (7)55000
20.8%
Uppercase Letter
ValueCountFrequency (%)
A10000
18.2%
C10000
18.2%
H10000
18.2%
R10000
18.2%
B5000
9.1%
E5000
9.1%
J5000
9.1%
Dash Punctuation
ValueCountFrequency (%)
-5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin320000
98.5%
Common5000
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l25000
 
7.8%
e25000
 
7.8%
a25000
 
7.8%
i25000
 
7.8%
t20000
 
6.2%
n20000
 
6.2%
c20000
 
6.2%
o20000
 
6.2%
r15000
 
4.7%
s15000
 
4.7%
Other values (14)110000
34.4%
Common
ValueCountFrequency (%)
-5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII325000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l25000
 
7.7%
e25000
 
7.7%
a25000
 
7.7%
i25000
 
7.7%
t20000
 
6.2%
n20000
 
6.2%
c20000
 
6.2%
o20000
 
6.2%
r15000
 
4.6%
s15000
 
4.6%
Other values (15)115000
35.4%

Interactions

2021-09-10T00:41:28.826525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:29.021526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:29.299528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:29.569532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:29.805535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:30.213529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:30.545535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:30.888532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:31.185532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:31.496525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:31.694565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:31.925530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:32.124528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:32.312013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:32.512584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:32.710582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:32.926578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:33.117612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:33.320589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:33.528580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:33.752610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:33.940582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:34.110579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:34.289617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:34.503614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:34.675614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:34.837617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:35.049609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:35.234611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:35.405579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:35.572611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:35.730604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:35.892586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:36.057611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:36.228577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:36.385580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:36.543911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:36.807915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:37.012882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:37.196917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:37.393783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:37.586806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:37.744809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:37.898810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:38.079776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:38.330786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:38.620776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:38.819781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:38.999812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:39.176310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:39.367277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:39.585273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:39.752310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:39.913309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:40.078273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:40.247304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:40.425306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:40.587310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:40.805295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:41.004303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:41.186308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:41.346903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-09-10T00:41:44.781472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:44.947468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-09-10T00:41:45.315434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:45.522438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-09-10T00:41:45.930437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-09-10T00:41:46.327438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-09-10T00:41:46.764506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:47.028519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:47.208507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:47.414509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:47.616503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:47.793539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:47.966539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:48.143543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:48.315536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:48.478539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:48.648530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:48.820508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:49.019532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:49.197503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:49.372539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:49.545537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:49.724507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:49.899537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:50.073509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:50.248537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:50.415506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:50.582512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:50.757518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:50.952513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:51.133542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:51.314541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:51.491582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:51.676548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:51.852544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:52.040545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:52.220541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:52.395576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:41:52.734577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-10T00:42:02.437579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-10T00:42:02.730546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-10T00:42:03.025580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-10T00:42:03.368546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-10T00:42:03.719543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-10T00:41:53.085546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-10T00:41:53.648542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

instance_idartist_nametrack_hashtrack_namepopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempoobtained_datevalencemusic_genre
020012Avenged Sevenfold0caajoOsHzQOZtIXitnRUNRequiem490.0001040.5322615730.9530.007650G#0.1190-5.667Major0.0394142.0164-Apr0.485Alternative
120018BADBADNOTGOOD0bdabO15YOj0iZPg2OujAwIn Your Eyes (feat. Charlotte Day Wilson)500.4300000.4952470550.5330.000672C0.1060-5.802Minor0.029579.714-Apr0.424Alternative
220042System Of A Down0blIe8ZSUusQfh4hvBNWoDF**k The System480.0021600.4741327330.9940.126000G#0.3140-1.884Major0.1030171.4334-Apr0.847Alternative
320057Jeremy Camp0CBM2iiBZmfKntDeQYboqUThere Will Be A Day480.1090000.3442794400.6670.000000F#0.1030-5.705Major0.038577.2914-Apr0.208Alternative
420066Three Days Grace0c1gHntWjKD7QShC8s99sqIt's All Over500.0030500.3212493200.8200.000000C0.3400-4.459Minor0.0422?4-Apr0.198Alternative
520077Dounia0hORgg6aV6GgDn5VQfZpclLOWKEY GRL (feat. Moroccan Doll)420.2900000.420-10.8240.000000A#0.1170-3.704Minor0.167079.7234-Apr0.458Alternative
620082Phil Wickham0JKY13K1Io2aqXJb96UyzXAt Your Name (Yahweh, Yahweh)410.0004650.2702344670.7660.000006A0.0874-5.202Major0.0394167.9254-Apr0.189Alternative
720083Always Never0IKYsgkoaylTKGzrlfrpu7It's Over470.2490000.6331424520.4510.002290D#0.1110-5.160Minor0.0603154.9834-Apr0.130Alternative
820087empty_field02zTH6cajCe9dXVyzimBduSo Far Away630.0120000.4072435730.8960.000000F#0.1960-2.935Major0.0630138.6973-Apr0.156Alternative
920124Toad The Wet Sprocket0i5el041vd6nxrGEU8QRxyFall Down420.1150000.4442023850.8860.000688C0.1640-5.705Major0.0335?4-Apr0.736Alternative

Last rows

instance_idartist_nametrack_hashtrack_namepopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempoobtained_datevalencemusic_genre
4999091664blink-1824y8a8CPp9ZzumIXZAluxgWMutt580.01520.5762038000.9350.000005A0.3490-3.963Major0.0514105.8014-Apr0.737Rock
4999191667The Mamas & The Papas5iBP84nYff7zzoYGSfOwgfCalifornia Dreamin' - Single Version750.35200.5521623730.6160.000000C#0.0501-9.785Minor0.0346112.442000000000014-Apr0.667Rock
4999291669Red Hot Chili Peppers5Hv1QAbRWrUSUHaT0CmnMSDani California770.01700.5532830000.8650.000010A0.2670-4.751Minor0.040996.1614-Apr0.735Rock
4999391687The Black Keys51kIxmllDJKx6H30tS1yQ1I'll Be Your Man510.43000.5971409330.7290.000281A0.1670-3.350Major0.0327120.0864-Apr0.712Rock
4999491700Theory of a Deadman53mrVsi49rLHIaKBiSvElGWake Up Call510.08140.2452350670.7150.000000G0.3410-6.192Major0.037275.0284-Apr0.367Rock
4999591712The Beatles7hpFYWL3cw5m4y70cce7ZbDay Tripper - Remastered 2015680.12000.6651690000.7820.000004F#0.1250-8.438Minor0.0307137.4534-Apr0.731Rock
4999691726The Beatles7JRN5xOUIrnI4crUMOt6X4I Feel Fine - Remastered 2015630.09040.5641393470.8270.000004G0.1270-7.089Major0.028389.847000000000014-Apr0.912Rock
4999791728Sugarland507bYMYfbm6sUS9iEAaeSdSomething More530.31300.5902167330.8500.000000E0.1160-4.419Major0.0582102.2654-Apr0.415Rock
4999891730Vinyl Theatre7IQsZqlZ53UIXFjzHOkraFBreaking Up My Bones610.01250.6081854290.8990.000000D0.2110-3.185Major0.0392105.032000000000014-Apr0.652Rock
4999991759MKTO7K8KNuwZAKKktkfPMosFsMAmerican Dream640.11400.5542257470.7670.000000C0.2310-5.043Major0.0497122.8464-Apr0.497Rock